Bibliographic record
Abstract
How should aggregate public expenditures be traded off against their financing costs? We incorporate public expenditures into a standard neoclassical growth setup with model policy choice as made by a government choosing tax rates and spending so that the resulting competitive equilibrium allocation maximizes consumer welfare. An additional key restriction that the government faces in our model is that it cannot commit to future policy. This restriction binds: current income taxes influence past savings decisions as well as past work decisions, and these effects are ignored by governments without access to commitment. We solve for equilibria where ‘reputational’ mechanisms are not operative: we characterize Markov-perfect equilibria of the dynamic game between successive governments. We characterize equilibria in terms of an intertemporal first-order condition (a ‘generalized Euler equation’, GEE) for the government and we use this condition both to gain insight into the nature of the equilibrium and as a basis for computation. The GEE reveals how the government optimally trades off tax wedges over time. For a calibrated economy, we find that when the tax base available to the government is capital income – an inelastic source of funds at any moment in time – the government still refrains from taxing at confiscatory rates. As a result, the economy is far from the mix of public and private goods that would be optimal in a static context; in return, steady-state savings are less distorted.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".